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Embedding OWA under preference ranking for DEA cross‐efficiency aggregation: Issues and procedures
Author(s) -
Oukil Amar
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22082
Subject(s) - ranking (information retrieval) , preference , rank (graph theory) , computer science , data envelopment analysis , embedding , extant taxon , process (computing) , matrix (chemical analysis) , data mining , sample (material) , artificial intelligence , mathematics , mathematical optimization , statistics , materials science , chemistry , chromatography , combinatorics , evolutionary biology , composite material , biology , operating system
Cross‐efficiency (CE) evaluation is an extension of data envelopment analysis (DEA) used for fully ranking decision‐making units (DMUs). The ranking process is normally performed on the matrix of CE scores. An ultimate efficiency score is computed for each DMU through an adequate amalgamation process. The preference ranking approach can be seen as an amalgamation technique based on the rank orders of the CE scores. In this paper, we review this approach by putting more emphasis on the aggregation aspect. We highlight the zero vote issue and we show that the latter has been neglected in the extant aggregation procedures. Consequently, we develop two ordered weighted averaging (OWA)‐based procedures that attempt to meet effectively the requirements of an aggregation mechanism while exploiting the positive properties of the preference‐ranking approach. The merits of the proposed procedures are evaluated on a sample of manufacturing systems by considering, for OWA weights generation, different OWA models with different orness degrees.